Ground Target Tracking Research Articles

Simultaneous Tracking of Multiple Ground Targets from a Multirotor Unmanned Aerial Vehicle

An algorithm for simultaneous tracking of multiple ground targets by an unmanned aerial vehicle is presented. The algorithm is specifically tailored toward multirotor vehicles, and it consists of a particle filter to predict target motion, a reference trajectory generator, and a finite-horizon model predictive controller for trajectory tracking. Two versions of the algorithm are proposed: for a vehicle equipped with a gimbaled camera, and for a vehicle equipped with a fixed camera. Furthermore, a target rejection algorithm is included to prune targets that inhibit accurate tracking of the majority of the target set. The tracking algorithm and multirotor vehicle dynamic model are first described, followed by example simulations for both the gimbaled and fixed-camera cases. Trade studies are presented, analyzing the effects of controller tuning parameters and camera field of view, as well as performance of the target rejection algorithm. In simulation experiments, real-world target data are used to improve simulation fidelity. Overall, results show that the algorithm is effective in capturing a set of ground targets within the field of view simultaneously when using either gimbaled or fixed-camera configurations, but performance is somewhat degraded in the fixed-camera case when target dynamics occur on timescales similar to tracking vehicle dynamics.

Feature Matching With an Adaptive Optical Sensor in a Ground Target Tracking System

We consider methods to address the optical feature-aided remote sensing tracking problem for vehicles in a challenging environment. Our approach is to apply the dynamic data driven application systems computing paradigm to implement control of an adaptive sensor. This adaptive sensor acquires a panchromatic image while simultaneously allowing the collection of visible-near infrared spectral data at specified pixels. This sensor holds the promise of delivering the increased accuracy of targeted spectral sensing without the enormous data volume of full spectral images. The target of interest is optimally imaged by the sensor based on the target's forecasted location and motion relative to the extracted content of the background. Background context is both extracted from the image and created from the OpenStreetMap road network. We describe the implementation of the tracking framework and testing of some of the components using simulated imagery created with the digital imaging and remote sensing image generation model. The Gaussian sum filter is employed to solve the data assimilation problem by forming a multimodel forecasting set that is used to increase the robustness and flexibility of tracking. For feature matching, we create an efficient sampling strategy that is informed by the viewing conditions to adaptively determine which pixels to measure spectrally in order to distinguish between different targets using a spectral distance measure.

Exploiting Doppler Blind Zone Information for Ground Moving Target Tracking with Bistatic Airborne Radar

One of the main challenges in the domain of ground target tracking based on measurements from airborne ground moving target indication (GMTI) radar is the masking of targets due to the Doppler blind zone of the sensor. This blind zone arises from the clutter cancellation by space-time adaptive processing (STAP) to separate moving target returns from the general clutter background. Low-Doppler targets can easily be masked by such a blind zone, leading to sequences of missed detections that strongly deteriorate the performance of a standard tracking filter. This work investigates the Doppler blind zone in the case of bistatic transmitter and receiver configurations and its impact on the tracking of ground targets. The knowledge on the Doppler blind zone is incorporated into a Bayesian unscented Kalman filter multiple hypothesis tracker (UKF-MHT) tracking filter that is also capable of using road-map information. Based on different simulation scenarios, it is demonstrated that in contrast to a monostatic configuration with sideways-looking radar, the blind zone width determined by the minimum detectable velocity is not constant in general. The obtained track results reveal the benefit of exploiting this additional knowledge, especially in combination complementary with road-map information if available.

Mathematical Model of Movement of the Observation and Tracking Head of an Unmanned Aerial Vehicle Performing Ground Target Search and Tracking

The paper presents the kinematics of mutual movement of an unmanned aerial vehicle (UAV) and a ground target. The controlled observation and tracking head (OTH) is a device responsible for observing the ground, searching for a ground target, and tracking it. The preprogrammed movement of the UAV on the circle with the simultaneous movement of the head axis on Archimedes’ spiral during searching for a ground target, both fixed (bunkers, rocket missiles launching positions, etc.) and movable (tanks, infantry fighting vehicles, etc.), is considered. Dynamics of OTH during the performance of the above mentioned activities is examined. Some research results are presented in a graphical form.

Intelligent Cooperative Control for Urban Tracking

We introduce an intelligent cooperative control system for ground target tracking in a cluttered urban environment with a team of autonomous Unmanned Air Vehicles (UAVs). We extend the work of Yu e...

Ground Target Tracking Using Aerial Images

The tracking of ground targets using aerial images was studied. A improved Kalman filter was derived for the tracking of ground targets. The novel feature of this improved filter were that the grey prediction equations and the road information have been incorporated to improve the accuracy of state estimates.The GM(1,1) (Grey Model) was introduced into Kalman prediction equations.The next value was forecasted by using few forward estimated values with a grey differential equation,which was baesd on the correction of mesurement covariance matrix. The tracking of the targets shows a satisfactory result.

Ground Target Optimal Tracking Model Based on Genetic Least Squares Support Vector Machine

Abstract The ground target optimal tracking is very important in the ground target tracking of UAV. In order to improve the ground target tracking performance of support vector machine, ground target optimal tracking model based on genetic least squares support vector machine in this paper. Least squares support vector machine can apply equality constraints for the error instead of inequality constraints used by support vector machine. Here, genetic algorithm is applied to select the appropriate parameters of least squares support vector machine.The process of ground target optimal tracking model based on genetic least squares support vector machine is given,the comparison of the ground target tracking distance and angle error among the three methods show that the ground target tracking effects of genetic least squares support vector machine are better than support vector machine and artificial neural network.

Ground Target Tracking Using UAV with Input Constraints

This paper provides a solution to the problem of ground target tracking using an unmanned aerial vehicle (UAV) with control input constraints. Target tracking control with input constraints is an important and challenging topic in the study of UAVs. In order to achieve precise target tracking in the presence of constant background wind and target motion, this paper proposes a saturated heading rate controller based on a guidance vector field while the airspeed is held constant. This proposed approach guarantees the global convergence of the UAV to a desired circular orbit around a target. To estimate unknown constant background wind and target motion, an adaptive observer with bounded estimate is developed. Simulation results demonstrate the effectiveness of the proposed approach.

Ground Target Tracking Control System for Unmanned Aerial Vehicles

The work here presented contributes to the development of ground target tracking control systems for fixed wing unmanned aerial vehicles (UAVs). The control laws are derived at the kinematic level,...

A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios.

Quaternion-based algorithm of ground target tracking by aircraft

The paper considers a problem of programmed tracking of ground targets by aircraft. Mathematical descriptions of various solutions to the problem are provided. Advantages of using quaternions in programmed tracking are discussed. Programmed tracking of ground targets is simulated for extreme cases.

Adversarial Ground Target Tracking Using UAVs with Input Constraints

This paper deals with the problem of adversarial ground target tracking using Unmanned Aerial Vehicles (UAVs) subject to input constraints. For adversarial ground target tracking, tracking performance and UAV safety are two important considerations during tracking controller design. In this paper, a bang-bang heading rate controller is proposed to achieve circular tracking around the target. Exposure avoidance of the UAV to the target and minimizing the exposure time are studied respectively in terms of the initial state of the UAV. The performance of the proposed controller in both cases is also analyzed. Simulation results demonstrate the effectiveness of the proposed approach.

A distributed estimator for on-road target tracking with lane estimation and identification

For ground target tracking with road constraints, the existing methods ignore the road width, which can lead to significant biases of the target state estimates when the road width is large. In this paper, based on the proposed novel two-dimensional (2D) road representation which additionally allows to model target lateral motion, a distributed estimator (DE) with lane estimation and identification is proposed. This estimator uses radar and image sensor based local estimates to provide a global estimate of the target displacement from the road axis. In local estimators, the updated lane estimations are used for iterations. The representative simulation results show that the proposed estimator achieves much better estimation performance than the mileage estimator (ME), and the lane identifier can effectively identify the lane which the target is in.

A Nonlinear Filter Coupled With Hospitability and Synthetic Inclination Maps for In-Surveillance and Out-of-Surveillance Tracking

This paper presents an algorithm for in-surveillance and out-of-surveillance mobile ground target tracking. In this respect, a nonlinear Bayesian estimation filter is presented. Then, an adaptive algorithm is derived to reduce remarkably the computational burden of this filter, while not degrading the accuracy of the state estimates. Additionally, an algorithm employing the concepts of hospitability and synthetic inclination maps is introduced and is coupled with the nonlinear Bayesian filter to track the mobile ground target once it goes out-of-surveillance. Loosely speaking, the hospitability map can be viewed as a terrain-based map defining a likelihood or a weight for each point on the earth's surface proportional to the ability of the target to move and maneuver at that location. On the other hand, the synthetic inclination map describes how the target favors certain regions within the search area, hence being synthetically inclined to move toward them.

Development of a vision-based ground target detection and tracking system for a small unmanned helicopter

It is undoubted that the latest trend in the unmanned aerial vehicles (UAVs) community is towards visionbased unmanned small-scale helicopter, utilizing the maneuvering capabilities of the helicopter and the rich information of visual sensors, in order to arrive at a versatile platform for a variety of applications such as navigation, surveillance, tracking, etc. In this paper, we present the development of a visionbased ground target detection and tracking system for a small UAV helicopter. More specifically, we propose a real-time vision algorithm, based on moment invariants and two-stage pattern recognition, to achieve automatic ground target detection. In the proposed algorithm, the key geometry features of the target are extracted to detect and identify the target. Simultaneously, a Kalman filter is used to estimate and predict the position of the target, referred to as dynamic features, based on its motion model. These dynamic features are then combined with geometry features to identify the target in the second-stage of pattern recognition, when geometry features of the target change significantly due to noise and disturbance in the environment. Once the target is identified, an automatic control scheme is utilized to control the pan/tilt visual mechanism mounted on the helicopter such that the identified target is to be tracked at the center of the captured images. Experimental results based on images captured by the small-scale unmanned helicopter, SheLion, in actual flight tests demonstrate the effectiveness and robustness of the overall system.

A novel interacting multiple model algorithm

For maneuvering target tracking, the interacting multiple model (IMM) algorithm employs a fixed model set. The performance of this algorithm depends on the model set adopted. The result of using too many models is as bad as the case of too few models. Therefore, a variable structure IMM (VSIMM) was presented and applied to ground target tracking. This algorithm improves performance and reduces computational load with using auxiliary information. But it is difficult to extend the VSIMM to other scenario (for example, aerial target), where there is not auxiliary information such as a map. A novel interacting multiple model (Novel-IMM) algorithm was presented to solve the problem of model set adaptation without auxiliary information. The Novel-IMM algorithm consists of N independent IMM filters operating in parallel, and each independent IMM filter also consists of multiple sub-filters, which operate interactively. In every time index, only one IMM output of a certain model set is used; but for a long time, the algorithm will alternatively choose an output of the model set to be the optimum final output. The Novel-IMM approach was illustrated in detail with an aerial complex maneuvering target tracking example.

A POMDP Framework for Coordinated Guidance of Autonomous UAVs for Multitarget Tracking

This paper discusses the application of the theory of partially observable Markov decision processes (POMDPs) to the design of guidance algorithms for controlling the motion of unmanned aerial vehicles (UAVs) with onboard sensors to improve tracking of multiple ground targets. While POMDP problems are intractable to solve exactly, principled approximation methods can be devised based on the theory that characterizes optimal solutions. A new approximation method called nominal belief-state optimization (NBO), combined with other application-specific approximations and techniques within the POMDP framework, produces a practical design that coordinates the UAVs to achieve good long-term mean-squared-error tracking performance in the presence of occlusions and dynamic constraints. The flexibility of the design is demonstrated by extending the objective to reduce the probability of a track swap in ambiguous situations.

Ground-Target Tracking in Multiple Cameras Using Collaborative Particle Filters and Principal Axis-Based Integration

Ground-Target Tracking in Multiple Cameras Using Collaborative Particle Filters and Principal Axis-Based Integration

Self-organization of unattended wireless acoustic sensor networks for ground target tracking

A distributed, self-organization algorithm for ground target tracking using unattended acoustic sensor network is developed. Instead of using microphone arrays, each sensor node in the sensor network uses only a single microphone as its sensing device. This design can greatly reduce the size and cost of each sensor node and allow more flexible deployment of the sensor network. The self-organization algorithm presented in this paper can dynamically select proper sensor nodes to form the localization sensor groups that can work as a virtual microphone array to perform energy efficient target localization and tracking. To achieve this, we use a time-delay based bearing estimation plus triangulation for source localization in the sensor network. Major error sources of the localization method like time delay estimation, bearing calculation and triangulation are analyzed and sensor selection criteria are developed. Based on these criteria and neighborhood information of each sensor node, a distributed self-organization algorithm is developed. Simulation results show the effectiveness of the proposed algorithm.

Interacting Multiple Model Particle-type Filtering Approaches to Ground Target Tracking

Ground maneuvering target tracking is a class of nonlinear and/or no-Gaussian filtering problem. A new interacting multiple model unscented particle filter (IMMUPF) is presented to deal with the problem. A bank of unscented particle filters is used in the interacting multiple model (IMM) framework for updating the state of moving target. To validate the algorithm, two groups of multiple model filters: IMM-type filters and particle-type multiple model filters, are compared for their capability in dealing with ground maneuvering target tracking problem. Simulation shows that particle-type filters outperform IMM-type filters in the estimate accuracy and the IMMUPF method relatively has much better performance than the IMMPF method.